Systems and methods for video-based non-contact tidal volume monitoring

ABSTRACT

The present invention relates to the field of medical monitoring, and in particular non-contact video monitoring to measure tidal volume of a patient. Systems, methods, and computer readable media are described for determining a region of interest of a patient and monitoring that region of interest to determine tidal volume of the patient. This may be accomplished using a depth sensing camera to monitor a patient and determine how their chest and/or other body parts are moving as the patient breathes. This sensing of movement can be used to determine the tidal volume measurement.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims priority to U.S. Provisional PatentApplication No. 62/614,763, filed Jan. 8, 2018, the disclosure of whichis incorporated herein by reference in its entirety.

BACKGROUND

Many conventional medical monitors require attachment of a sensor to apatient in order to detect physiologic signals from the patient andtransmit detected signals through a cable to the monitor. These monitorsprocess the received signals and determine vital signs such as thepatient's pulse rate, respiration rate, and arterial oxygen saturation.For example, a pulse oximeter is a finger sensor that may include twolight emitters and a photodetector. The sensor emits light into thepatient's finger and transmits the detected light signal to a monitor.The monitor includes a processor that processes the signal, determinesvital signs (e.g., pulse rate, respiration rate, arterial oxygensaturation), and displays the vital signs on a display.

Other monitoring systems include other types of monitors and sensors,such as electroencephalogram (EEG) sensors, blood pressure cuffs,temperature probes, air flow measurement devices (e.g., spirometer), andothers. Some wireless, wearable sensors have been developed, such aswireless EEG patches and wireless pulse oximetry sensors.

Video-based monitoring is a new field of patient monitoring that uses aremote video camera to detect physical attributes of the patient. Thistype of monitoring may also be called “non-contact” monitoring inreference to the remote video sensor, which does not contact thepatient. The remainder of this disclosure offers solutions andimprovements in this new field.

SUMMARY

In an embodiment described herein, a method of determining tidal volumeof a patient, includes receiving, by a processor, at least one imageincluding depth information for at least part of the patient. The methodfurther includes determining, by the processor, a reference point on thepatient. The method further includes determining, by the processor, aregion of interest based at least in part on the reference point. Theregion of interest corresponds to a trunk area of the patient. Themethod further includes monitoring changes in the depth information inthe region of interest over time. The method further includes mappingthe monitored changes in depth information to a tidal volume for thepatient.

In some embodiments, the region of interest is further defined based onat least one body coordinate determined from the reference point.

In some embodiments, each of the at least one body coordinatescorrespond to a location on a body of the patient, and the location onthe body of the at least one body coordinate is at least one of ashoulder, a hip, a neck, a chest, and a waist.

In some embodiments, the region of interest is further determined basedon a distance of various portions of the patient from a camera thatcaptures the at least one image.

In some embodiments, the region of interest is further determined bydiscarding various portions of a flood fill in response to determiningthat the patient is rotated such that the patient is not orthogonal to aline of sight of a camera that captures the at least one image.

In some embodiments, the region of interest is further determined bydetermining that the trunk area of the patient is partially obscured andexcluding a partially obscured region from the region of interest.

In some embodiments, the at least one image is captured by a firstcamera, and at least a second image comprising at least part of thepatient is captured by a second camera.

In some embodiments, the method further includes determining, by theprocessor, a second region of interest of the patient based on at leastthe second image.

In some embodiments, the method further includes determining, by theprocessor, a second region of interest of the patient from the at leastone image.

In some embodiments, the region of interest is a different size than thesecond region of interest.

In another embodiment described herein, a video-based method ofmonitoring a patient includes receiving, by a processor, a video feedincluding a plurality of images captured at different times. At least aportion of a patient is captured by the video feed. The method furtherincludes determining, by the processor, a region of interest of thehuman patient on the video feed. The region of interest corresponds to atrunk area of the patient. The method further includes measuring, by theprocessor, changes to the region of interest over time. The methodfurther includes determining, by the processor, based on the changes tothe region of interest, a tidal volume of the patient.

In some embodiments, the method further includes comparing, by theprocessor, the tidal volume determined based on the changes to theregion of interest to an output of an air flow measurement device andcalibrating, by the processor, the tidal volume determination based onthe comparison.

In some embodiments, the method further includes receiving, by theprocessor, demographic information about the patient and adjusting thetidal volume determination based on the demographic information.

In some embodiments, the demographic information comprises at least oneof a sex, height, weight, body mass index (BMI), and age of the patient.

In some embodiments, a size of the region of interest is at leastpartially dependent on a distance of the patient from a camera thatcaptures the video feed.

In some embodiments, the method further includes determining, using theprocessor, a change in the tidal volume of the patient over time.

In some embodiments, the method further includes determining, using theprocessor, based on the change in the tidal volume of the patient, apotential hypoventilation condition.

In some embodiments, the region of interest is configured based on anorientation of the patient with respect to a camera that captures thevideo feed.

In some embodiments, the tidal volume of the patient is determined basedon an orientation of the patient with respect to a camera that capturesthe video feed.

In some embodiments, the video feed is captured by a first camera, and asecond video feed is captured by a second camera, and at least a secondportion of the patient is captured by the second video feed.

In some embodiments, the method further includes determining, by theprocessor, a second region of interest of the patient based on thesecond video feed.

In some embodiments, the tidal volume is further determined based onchanges to the second region of interest over time.

In some embodiments, the method further includes determining, by theprocessor, a second region of interest of the patient from the videofeed.

In some embodiments, the region of interest is a different size than thesecond region of interest.

In some embodiments, the tidal volume is further determined based onchanges to the second region of interest over time.

In a further aspect, which may be provided independently, there isprovided an apparatus for determining tidal volume of a patient, theapparatus comprising a processor configured to: receive at least oneimage comprising depth information for at least a portion of thepatient; determine a reference point on the patient; determine a regionof interest based at least in part on the reference point, wherein theregion of interest corresponds to a trunk area of the patient; monitorchanges in the depth information in the region of interest over time;and map the monitored changes in depth information to a tidal volume forthe patient.

In a further aspect, which may be provided independently, there isprovided an apparatus for video-based monitoring of a patient, theapparatus comprising a processor configured to: receive a video feedcomprising a plurality of images captured at different times, wherein atleast a portion of a patient is captured within the video feed;determine a region of interest of the patient on the video feed, whereinthe region of interest corresponds to a trunk area of the patient;measure changes to the region of interest over time, and determine atidal volume of the patient based on the changes to the region ofinterest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a video-based patient monitoring systemaccording to various embodiments described herein.

FIG. 2 is a block diagram illustrating a computing device, a server, andan image capture device according to various embodiments describedherein.

FIG. 3 is an image captured by a camera according to various embodimentsdescribed herein.

FIG. 4 is a graph showing a tidal volume calculation over time accordingto various embodiments described herein.

FIG. 5 is a diagram showing how tidal volume associated with a region ofinterest (ROI) may be calculated according to various embodimentsdescribed herein.

FIG. 6 is a flowchart of a method for determining a region of interest(ROI) and measuring tidal volume according to various embodimentsdescribed herein.

FIGS. 7A-7D are diagrams showing examples of different ROIs fordifferent sized patients according to various embodiments describedherein.

FIG. 8 is a diagram showing a complex ROI according to variousembodiments described herein.

FIG. 9 is a diagram showing a patient with a superimposed skeletonaccording to various embodiments described herein.

FIG. 10 is a diagram showing a patient with a superimposed skeleton andROI according to various embodiments described herein.

FIG. 11 is a diagram showing a patient with an ROI turned to face afirst direction according to various embodiments described herein.

FIG. 12 is a diagram showing a patient with an ROI turned to face asecond direction according to various embodiments described herein.

FIG. 13 is a diagram showing a patient with an ROI that has been floodfilled according to various embodiments described herein.

FIG. 14 is a diagram showing an implementation of a depth mask todetermine an ROI according to various embodiments described herein.

FIG. 15 is a diagram showing a patient with an ROI turned to face afirst direction, where the ROI has been flood filled and discards thearms according to various embodiments described herein.

FIG. 16 is a diagram showing a patient with an ROI turned to face asecond direction, where the ROI has been flood filled and discards thearms according to various embodiments described herein.

FIG. 17 is a diagram showing a patient with an ROI that does not includethe patient's hand according to various embodiments described herein.

FIG. 18 is a diagram showing a patient with an ROI where the arms andhead have been excluded according to various embodiments describedherein.

FIG. 19 is a diagram showing a patient with an ROI where the arms andhead have been excluded and the patient is turned to face a firstdirection according to various embodiments described herein.

FIG. 20 is a diagram showing a patient with an ROI where the arms andhead have been excluded and the patient is turned to face a seconddirection according to various embodiments described herein.

FIG. 21 is a diagram showing a patient with an ROI that does not includethe patient's hands according to various embodiments described herein.

FIG. 22 is a graph showing tidal volume measured by an air flowmeasurement device as compared to tidal volume measured by non-contactvideo monitoring according to various embodiments described herein.

FIG. 23 is a graph showing tidal volume measurements and a respiratorycompromise threshold according to various embodiments described herein.

FIG. 24 is a graph showing tidal volume measurements and a thresholdtidal volume indicating hypoventilation according to various embodimentsdescribed herein.

FIG. 25 is a graph showing a measured minute volume that can be used tocalculate a degree of compromise according to various embodimentsdescribed herein.

FIG. 26 is a diagram showing an ROI with a flood fill region accordingto various embodiments described herein.

FIG. 27 is a diagram showing a patient at an original position accordingto various embodiments described herein.

FIG. 28 is a diagram showing a patient at an angle to a line of sight ofa camera according to various embodiments described herein.

FIG. 29 is a diagram showing a representation of a patient from aboveaccording to various embodiments described herein.

FIG. 30 is a diagram showing a representation of a patient at an angleto a line of sight of a camera from above according to variousembodiments described herein.

FIG. 31 is a diagram showing apparent movement of an ROI of a patientorthogonal to a line of sight of a camera according to variousembodiments described herein.

FIG. 32 is a diagram showing apparent movement of an ROI of a patientthat is not orthogonal to a line of sight of a camera according tovarious embodiments described herein.

FIG. 33 is a diagram showing an angle at which a patient's ROI is notorthogonal to a line of sight of a camera according to variousembodiments described herein.

FIG. 34 is a diagram showing a representation of different depththresholds associated with a patient orthogonal to a line of sight of acamera according to various embodiments described herein.

FIG. 35 is a diagram showing a representation of unadjusted depththresholds with respect to a patient that is not orthogonal to a line ofsight of a camera according to various embodiments described herein.

FIG. 36 is a diagram showing a representation of adjusted depththresholds with respect to a patient that is not orthogonal to a line ofsight of a camera according to various embodiments described herein.

FIG. 37 is a diagram showing an alternate method for adjusting depththresholds with respect to a patient based on locations of shoulders ofthe patient according to various embodiments described herein.

FIG. 38 is a diagram showing an ROI of a patient according to variousembodiments described herein.

FIG. 39 is a diagram showing an ROI of a patient with a partialobstruction of the patient's hands according to various embodimentsdescribed herein.

FIG. 40 is a diagram showing a patient with a three-dimensional meshsuperimposed over the patient according to various embodiments describedherein.

FIG. 41 is a diagram showing an ROI of a patient with an obscured areaaccording to various embodiments described herein.

FIG. 42 is a diagram showing an ROI with an excluded obscured areaaccording to various embodiments described herein.

FIG. 43 is another diagram showing an ROI with an excluded obscured areaaccording to various embodiments described herein.

FIG. 44 is a diagram showing a two-camera system for determining ROIs ofa patient and/or measuring tidal volume of the patient according tovarious embodiments described herein.

FIG. 45 is a diagram showing a patient with two differently sized ROIsfor measuring tidal volume according to various embodiments describedherein.

FIG. 46 is a flowchart showing a method for determining tidal volumeusing two differently sized ROIs according to various embodimentsdescribed herein.

DETAILED DESCRIPTION

The present invention relates to the field of medical monitoring, and inparticular non-contact monitoring of patient with regard to respiratorymonitoring. Systems, methods, and computer readable media are describedherein for determining a region of interest of a patient and monitoringthat region of interest to determine tidal volume of the patient. Thesystems, methods, and computer readable media disclosed herein have thepotential to improve recordkeeping, improve patient care, reduce errorsin vital sign measurements, increase frequency and accuracy ofrespiratory monitoring, help healthcare providers better characterizeand respond to adverse medical conditions indicated by decreased tidalvolume (e.g., hypoventilation), and generally improve monitoring ofpatients, along with many other potential advantages discussed below.Tidal volume measurement/monitoring can further be helpful in thefollowing areas: respiratory compromise, non-invasive ventilation,volume capnography, neonatal monitoring, pain management, post-surgerymonitoring/treatment, and more. In particular, arterial blood oxygensaturation is a lagging indicator of respiratory compromise; it may take60 seconds or longer for oxygen saturation levels to drop after apatient stops breathing. By monitoring breathing as disclosed herein,patients who have slow, shallow, or stopped breathing can be attended tomore quickly, potentially saving lives and leading to better treatment.

Improvements disclosed herein can greatly increase the ability to detector measure respiratory compromise, thereby increasing the level of carehealthcare professionals can provide to patients. For example, theability to determine the nature of respiration of a patient allows forthe determination of progression of a disease state and/or impendingcomplication including imminent respiratory arrest.

Beneficially, the systems, methods, and computer readable mediadisclosed herein provide for enhanced ways of measuring tidal volume ofa patient using non-contact monitoring. With contact-based monitoring,tidal volume can be measured by utilizing an obtrusive maskincorporating a specialized flow measurement device. These masks andflow devices can be bulky and uncomfortable, and accordingly, this typeof device may not be routinely used on patients. Additionally, even whenit is used, it may not be used for long periods of time, and thereforemay not be suitable for long term monitoring of tidal volume of apatient.

As described herein, non-contact video monitoring can be utilized todetermine a volume of airflow indicative of tidal volume of a patient.For example, this may be accomplished using a depth sensing camera tomonitor a patient and determine movements of their chest and/or otherbody parts as the patient breathes. This sensing of movement can be usedto determine a tidal volume measurement. Accordingly, disclosed hereinare systems, methods, and computer readable media for determining atidal volume measurement using non-contact video monitoring of apatient. Furthermore, the systems, methods, and computer readable mediadisclosed herein accommodate patients with different characteristics anddisease states, enabling more accurate patient-specific measurementsacross many different clinical scenarios.

FIG. 1 is a schematic view of a video-based patient monitoring system200 and a patient 212 according to an embodiment of the invention. Thesystem 200 includes a non-contact detector 210 placed remote from thepatient 212. In this embodiment, the detector 210 includes a camera 214,such as a video camera. The camera 214 is remote from the patient, inthat it is spaced apart from and does not contact the patient 212. Thecamera 214 includes a detector exposed to a field of view 216 thatencompasses at least a portion of the patient 212.

The camera 214 generates a sequence of images over time. The camera 214may be a depth sensing camera, such as a Kinect camera from MicrosoftCorp. (Redmond, Wash.). A depth sensing camera can detect a distancebetween the camera and objects in its field of view. Such informationcan be used, as disclosed herein, to determine that a patient is withinthe field of view of the camera 214 and determine a region of interest(ROI) to monitor on the patient. Once an ROI is identified, that ROI canbe monitored over time, and the change in depth of points within the ROIcan represent movements of the patient associated with breathing.Accordingly, those movements, or changes of points within the ROI, canbe used to determine tidal volume as disclosed herein.

In some embodiments, the system determines a skeleton outline of apatient to identify a point or points from which to extrapolate an ROI.For example, a skeleton may be used to find a center point of a chest,shoulder points, waist points, and/or any other points on a body. Thesepoints can be used to determine an ROI. For example, an ROI may bedefined by filling in area around a center point of the chest. Certaindetermined points may define an outer edge of an ROI, such as shoulderpoints. In other embodiments, instead of using a skeleton, other pointsare used to establish an ROI. For example, a face may be recognized, anda chest area inferred in proportion and spatial relation to the face. Inother embodiments as described herein, the system may establish the ROIaround a point based on which parts are within a certain depth range ofthe point. In other words, once a point is determined that an ROI shouldbe developed from, the system can utilize the depth information from adepth sensing camera to fill out the ROI as disclosed herein. Forexample, if a point on the chest is selected, depth information isutilized to determine an ROI area around the determined point that is asimilar distance from the depth sensing camera as the determined point.This area is likely to be a chest. Using threshold depths in relation toa determined point is further shown and described below at least withrespect to FIGS. 14 and 33-37 .

In another example, a patient may wear a specially configured piece ofclothing that identifies points on the body such as shoulders or thecenter of the chest. A system may identify those points by identifyingthe indicating feature of the clothing. Such identifying features couldbe a visually encoded message (e.g., bar code, QR code, etc.), or abrightly colored shape that contrasts with the rest of the patient'sclothing, etc. In some embodiments, a piece of clothing worn by thepatient may have a grid or other identifiable pattern on it to aid inrecognition of the patient and/or their movement. In some embodiments,the identifying feature may be stuck on the clothing using a fasteningmechanism such as adhesive, a pin, etc. For example, a small sticker maybe placed on a patient's shoulders and/or center of the chest that canbe easily identified from an image captured by a camera. In someembodiments, the indicator may be a sensor that can transmit a light orother information to a camera that enables its location to be identifiedin an image so as to help define an ROI. Therefore, different methodscan be used to identify the patient and define an ROI.

In some embodiments, the system may receive a user input to identify astarting point for defining an ROI. For example, an image may bereproduced on an interface, allowing a user of the interface to select apatient for monitoring (which may be helpful where multiple humans arein view of a camera) and/or allowing the user to select a point on thepatient from which the ROI can be determined (such as a point on thechest). Other methods for identifying a patient, points on the patient,and defining an ROI may also be used, as described further below.

In various embodiments, the ROI or portions of the ROI may be determinedto move in accordance with respiratory patterns, to determine a tidalvolume of the patient, as described further below.

The detected images are sent to a computing device through a wired orwireless connection 220. The computing device includes a processor 218,a display 222, and hardware memory 226 for storing software and computerinstructions. Sequential image frames of the patient are recorded by thevideo camera 214 and sent to the processor 218 for analysis. The display222 may be remote from the camera 214, such as a video screen positionedseparately from the processor and memory. Other embodiments of thecomputing device may have different, fewer, or additional componentsthan shown in FIG. 1 . In some embodiments, the computing device may bea server. In other embodiments, the computing device of FIG. 1 may beadditionally connected to a server (e.g., as shown in FIG. 2 anddiscussed below). The captured images/video can be processed or analyzedat the computing device and/or a server to determine tidal volume of thepatient 212 as disclosed herein.

FIG. 2 is a block diagram illustrating a computing device 300, a server325, and an image capture device 385 according to an embodiment of theinvention. In various embodiments, fewer, additional and/or differentcomponents may be used in a system. The computing device 300 includes aprocessor 315 that is coupled to a memory 305. The processor 315 canstore and recall data and applications in the memory 305, includingapplications that process information and send commands/signalsaccording to any of the methods disclosed herein. The processor 315 mayalso display objects, applications, data, etc. on an interface/display310. The processor 315 may also receive inputs through theinterface/display 310. The processor 315 is also coupled to atransceiver 320. With this configuration, the processor 315, andsubsequently the computing device 300, can communicate with otherdevices, such as the server 325 through a connection 370 and the imagecapture device 385 through a connection 380. For example, the computingdevice 300 may send to the server 325 information determined about apatient from images captured by the image capture device 385 (such as acamera), such as depth information of a patient in an image or tidalvolume information determined about the patient, as disclosed herein.The computing device 300 may be the computing device of FIG. 1 .Accordingly, the computing device 300 may be located remotely from theimage capture device 385, or it may be local and close to the imagecapture device 385 (e.g., in the same room). In various embodimentsdisclosed herein, the processor 315 of the computing device 300 mayperform the steps disclosed herein. In other embodiments, the steps maybe performed on a processor 335 of the server 325. In some embodiments,the various steps and methods disclosed herein may be performed by bothof the processors 315 and 335. In some embodiments, certain steps may beperformed by the processor 315 while others are performed by theprocessor 335. In some embodiments, information determined by theprocessor 315 may be sent to the server 325 for storage and/or furtherprocessing.

In some embodiments, the image capture device 385 is a remote sensingdevice such as a video camera. In some embodiments, the image capturedevice 385 may be some other type of device, such as a proximity sensoror proximity sensor array, a heat or infrared sensor/camera, asound/acoustic or radiowave emitter/detector, or any other device thatmay be used to monitor the location of a patient and an ROI of a patientto determine tidal volume. Body imaging technology may also be utilizedto measure tidal volume according to the methods disclosed herein. Forexample, backscatter x-ray or millimeter wave scanning technology may beutilized to scan a patient, which can be used to define an ROI andmonitor movement for tidal volume calculations. Advantageously, suchtechnologies may be able to “see” through clothing, bedding, or othermaterials while giving an accurate representation of the patient's skin.This may allow for more accurate tidal wave measurements, particularlyif the patient is wearing baggy clothing or is under bedding. The imagecapture device 385 can be described as local because it is relativelyclose in proximity to a patient so that at least a part of a patient iswithin the field of view of the image capture device 385. In someembodiments, the image capture device 385 can be adjustable to ensurethat the patient is captured in the field of view. For example, theimage capture device 385 may be physically movable, may have achangeable orientation (such as by rotating or panning), and/or may becapable of changing a focus, zoom, or other characteristic to allow theimage capture device 385 to adequately capture a patient for ROIdetermination and tidal volume monitoring. In various embodiments, afteran ROI is determined, a camera may focus on the ROI, zoom in on the ROI,center the ROI within a field of view by moving the camera, or otherwisemay be adjusted to allow for better and/or more accuratetracking/measurement of the movement of a determined ROI.

The server 325 includes a processor 335 that is coupled to a memory 330.The processor 335 can store and recall data and applications in thememory 330. The processor 335 is also coupled to a transceiver 340. Withthis configuration, the processor 335, and subsequently the server 325,can communicate with other devices, such as the computing device 300through the connection 370.

The devices shown in the illustrative embodiment may be utilized invarious ways. For example, any of the connections 370 and 380 may bevaried. Any of the connections 370 and 380 may be a hard-wiredconnection. A hard-wired connection may involve connecting the devicesthrough a USB (universal serial bus) port, serial port, parallel port,or other type of wired connection that can facilitate the transfer ofdata and information between a processor of a device and a secondprocessor of a second device. In another embodiment, any of theconnections 370 and 380 may be a dock where one device may plug intoanother device. In other embodiments, any of the connections 370 and 380may be a wireless connection. These connections may take the form of anysort of wireless connection, including, but not limited to. Bluetoothconnectivity, Wi-Fi connectivity, infrared, visible light, radiofrequency (RF) signals, or other wireless protocols/methods. Forexample, other possible modes of wireless communication may includenear-field communications, such as passive radio-frequencyidentification (RFID) and active RFID technologies. RFID and similarnear-field communications may allow the various devices to communicatein short range when they are placed proximate to one another. In yetanother embodiment, the various devices may connect through an internet(or other network) connection. That is, any of the connections 370 and380 may represent several different computing devices and networkcomponents that allow the various devices to communicate through theinternet, either through a hard-wired or wireless connection. Any of theconnections 370 and 380 may also be a combination of several modes ofconnection.

The configuration of the devices in FIG. 2 is merely one physical systemon which the disclosed embodiments may be executed. Other configurationsof the devices shown may exist to practice the disclosed embodiments.Further, configurations of additional or fewer devices than the onesshown in FIG. 2 may exist to practice the disclosed embodiments.Additionally, the devices shown in FIG. 2 may be combined to allow forfewer devices than shown or separated such that more than the threedevices exist in a system. It will be appreciated that many variouscombinations of computing devices may execute the methods and systemsdisclosed herein. Examples of such computing devices may include othertypes of medical devices and sensors, infrared cameras/detectors, nightvision cameras/detectors, other types of cameras, radio frequencytransmitters/receivers, smart phones, personal computers, servers,laptop computers, tablets, blackberries, RFID enabled devices, or anycombinations of such devices.

FIG. 3 is an image captured by a camera according to various embodimentsdescribed herein. In this particular example, the image in FIG. 3 is adepth image or depth map captured by a depth sensing camera, such as aKinect camera from Microsoft. The depth image includes information aboutthe distance from the camera to each point in the image. This type ofimage or map can be obtained by a stereo camera, a camera cluster,camera array, or a motion sensor. When multiple depth images are takenover time in a video stream, the video information includes the movementof the points within the image, as they move toward and away from thecamera over time.

The image includes a patient 390 and a region of interest (ROI) 395. TheROI 395 can be used to determine a volume measurement from the chest ofthe patient 390. The ROI 395 is located on the patient's chest. In thisexample, the ROI 395 is a square box. In various embodiments, other ROIsmay be different shapes. Because the image includes depth data, such asfrom a depth sensing camera, information on the spatial location of thepatient 390, and therefore the patient's chest and the ROI 395, can alsobe determined. This information can be contained within a matrix, forexample. As the patient 390 breathes, the patient's chest moves towardand away from the camera, changing the depth information associated withthe images over time. As a result, the location information associatedwith the ROI 395 changes over time. The position of individual pointswithin the ROI 395 may be integrated across the area of the ROI 395 toprovide a change in volume over time as shown in FIGS. 4 and 5 . FIG. 4is a graph showing a tidal volume calculation over time according tovarious embodiments described herein.

FIG. 5 is a diagram showing how tidal volume associated with a region ofinterest (ROI) may be calculated according to various embodimentsdescribed herein. Vectors associated with points within the ROI 395 aredepicted in FIG. 5 , where a schematic of the box values are shown tochange over time. For example, these vectors represent movement of apatient's chest toward a camera as the patient's chest expands forwardwith inhalation. Similarly, the vectors will then move backward, awayfrom the camera, when the patient's chest contrasts with exhalation.This movement forward and backward can be tracked to determine arespiration rate. Furthermore, this movement forward and backward can beintegrated to determine a tidal volume, as shown in FIG. 5 . Byintegrating the perpendicular vector values H(x,y,t) across the x and ycoordinates of the box, the instantaneous volume may be generated asfollows in Equation 1:V(t)=∫∫H(x,y,t)dxdy  [1]

The initial values of H may be set to zero when the analysis of the boxis first activated. Therefore, a volume signal V(t) such as the oneshown in FIG. 4 may be generated. The volume signal in FIG. 4 shows fourshallow breaths followed by two deep breaths then another shallow breathundertaken by the patient 390. The peaks and valleys of the signal inFIG. 4 can be used to identify individual breaths, the size ofindividual breaths, and a patient's overall respiration rate. Furthermethods as disclosed herein can be utilized to calibrate thesemeasurements to produce a true tidal volume of the patient 390.

FIG. 6 is a flowchart of a method 600 for determining a region ofinterest (ROI) and measuring tidal volume according to variousembodiments described herein. The method 600 includes receiving at leastone image comprising at least part of a patient at 605. The method 600further includes determining a skeleton or reference point of thepatient at 610. The method 600 further includes determining a region ofinterest (ROI) based at least in part on the skeleton or reference pointat 615. In some embodiments, methods or measurements other than askeleton may be used to determine the ROI. For example, the system mayidentify points on the patient's body (such as shoulders, head, neck,waist, etc.) that correspond to specific places that can be used as acentroid, reference, or flood fill point for forming an ROI. The systemmay also use information from a depth sensing camera to determine otherinformation about a patient. For example, the system may determine howfar away from the camera a patient is using a depth sensing camera orother depth sensing technology. Once that information is known, thesystem can use the ROI and/or other points of the body that aredetermined to calculate approximate size of a body or parts of the body.For example, the system may map determined ROI dimensions or otherdetermined information about a patient to approximate size, height,weight, BMI, age, sex, or another characteristic of a patient.

The method 600 further includes measuring changes to the ROI over timeat 620. This may be accomplished in various ways as disclosed herein.The method 600 further includes determining, based on the changes to theregion of interest, a tidal volume of the patient at 625. Thisdetermination can be performed in using any of the methods, systems, andcomputer readable media disclosed herein.

In some embodiments, the volume signal from the non-contact system mayneed to be calibrated to provide an absolute measure of volume. Forexample, the volume signal obtained from integrating points in a ROIover time may accurately track a patient's tidal volume and may beadjusted by a calibration factor. The calibration or correction factorcould be a linear relationship such as a linear slope and intercept, acoefficient, or other relationships. As an example, the volume signalobtained from a video camera may under-estimate the total tidal volumeof a patient, due to underestimating the volume of breath that expands apatient's chest backward, away from the camera, or upward orthogonal tothe line of sight of the camera. Thus, the non-contact volume signal maybe adjusted by simply adding or applying a correction or calibrationfactor. This correction factor can be determined in a few differentways. In one embodiment, an initial reference measurement is taken witha separate flow measurement device. For example, the tidal volume of thepatient may be measured using a flow measurement device (e.g. aspirometer) to produce a reference tidal volume over a short calibrationor test time frame (such as 3 to 4 breaths). The V(t) signal (alsoreferred to herein as the volume signal, the tidal volume, and/or thetidal volume signal) over the same time frame is compared to thereference tidal volume, and a calibration factor is determined so thatthe range of V(t) matches the reference tidal volume measured by theflow measurement device. After a few calibration breaths through theflow measurement device, it may be removed from the patient. The V(t)volume signal measured thereafter from the video feed is adjusted usingthe calibration factor determined during the initial calibration phase.

In some embodiments, demographic data about a patient may be used tocalibrate the volume signal. From a knowledge of the patient'sdemographic data, which may include height, weight, chest circumference,BMI, age, sex, etc., a mapping from the measured V(t) to an actual tidalvolume signal may be determined. For example, patients of smaller heightand/or weight may have less of a weighting coefficient for adjustingmeasured V(t) for a given ROI box size than patients of greater heightand/or weight. Different corrections or mappings may also be used forother factors, such as whether the patient is under bedding, type/styleof clothing worn by a patient (e.g., t-shirt, sweatshirt, hospital gown,dress, v-neck shirt/dress, etc.), thickness/material ofclothing/bedding, a posture of the patient, and/or an activity of thepatient (e.g., eating, talking, sleeping, awake, moving, walking,running, etc.). FIGS. 7A-7D are diagrams showing examples of differentROIs for different sized patients according to various embodimentsdescribed herein. In other words, even though the ROI boxes of each ofthe patients in FIGS. 7A and 7B are the same size, the measured V(t) canbe adjusted according to the actual size of the patient so that thereported V(t) is more accurate. Thus, if the true tidal volume(V_(True)) is related to the video measured tidal volume from the ROI(V_(ROI)) as follows in Equation 2:V _(True) =K·V _(ROI) +C  [2]where K and C are constants, then K and/or C may be varied according todemographic information. Note that C may be zero or non-zero.

Alternatively, the ROI size may be set according to the patientdemographics, i.e., patients of smaller height and/or weight may use asmaller ROI size than patients of greater height and/or weight, such asshown in FIGS. 7C and 7D. Thus, the ROI boxes are scaled according tothe patient's size to provide a consistency of the measured part of thebody from patient to patient. This scaling can be done based on inputsof a patient's demographics, or may be done based on sensing a differentsize patient in the image captured by the camera, or by input from auser such as clinician.

The ROI sizes may also differ according to the distance of the patientfrom the camera system. The ROI dimensions may vary linearly with thedistance of the patient from the camera system. This ensures that theROI scales according with the patient and covers the same part of thepatient regardless of the patient's distance from the camera. When theROI is scaled correctly based on the patient's position in the field ofview, the resulting tidal volume calculation from the volume signal V(t)can be maintained, regardless of where the patient is in the field ofview. That is, a larger ROI when the patient is closer to the camera,and a smaller ROI when the same patient is further from the camera,should result in the same V(t) calculation. This is accomplished byapplying a scaling factor that is dependent on the distance of thepatient (and the ROI) from the camera. In order to properly measure thetidal volume of a patient, the actual size of an ROI (the area of theROI) is determined. Then movements of that ROI (see, e.g., FIG. 5 ) aremeasured. The measured movements of the ROI and the actual size of theROI are then used to calculate a tidal volume. Because a patient'sdistance from a camera can change, an ROI associated with that patientcan appear to change in size in an image from a camera. However, usingthe depth sensing information captured by a depth sensing camera orother type of depth sensor, the system can determine how far away fromthe camera the patient (and their ROI) actually is. With thisinformation, the actual size of the ROI can be determined, allowing foraccurate measurements of tidal volume regardless of the distance of thecamera to the patient.

Instead of a box of a preset or scaled size, the ROI may instead have amore complex morphology to capture the whole chest region of thepatient. An example of this is shown in FIG. 8 , which is a diagramshowing a complex ROI according to various embodiments described herein.This approach may use a flood field method and/or a method whichidentifies the outline of the patient to determine the ROI.

Another type of smart ROI determination may use respiration rate (RR)modulations power analysis. This compares a power while breathing to apower while not breathing to filter noise and determine more accurateROIs and tidal volumes. In a method, a center of the chest is locatedbased on an image of the patient captured by the camera. A small area inthe center of the chest is identified where a good respiratorymodulation can be extracted. To do so, the chest may be monitored overtime to determine a point where that good respiratory modulation islocated. The movement of various points on the chest may be comparedwith a known or expected respiration rate to ensure that a good point isselected. Then, the full frame/field processing can be performed. Aquality metric using a power ratio (Prr/Pnot-rr) will yield a heatmapwhich can be reduced to an ROI by using a dynamic threshold. Points thatmodulate at the respiration rate and above a threshold amplitude areadded to the ROI, and points that do not modulate at that rate or atthat amplitude are discarded. This ROI can be updated dynamically, sothat the ROI is continually refreshing to capture the portions of thechest that are moving with breaths, or to track the chest as the patientmoves across the field of view. Because the distance to the camera ofeach point on the chest is known, expected dimensions of the ROI mayalso be inferred. That is, because the general shape of a chest isknown, a system may also make sure that portions of an image included inan ROI fit into an expected human chest or trunk shape. The portionssingled out as likely to be human/chest trunk may be determined based onthe depth information from the image. The system may also include in anROI points on the chest that fit into a predetermined distance thresholdfrom the camera, as discussed herein (see, e.g., discussion regardingFIGS. 14 and 33-37 ). This predetermined distance threshold can be setbased on known expected human chest/trunk sizes and dimensions.Furthermore, a dynamic threshold for the heatmap produces a complexchest ROI of expected dimension, and shape. In addition, in someembodiments as disclosed herein, an ROI may include more than onenon-connected or non-contiguous areas. Those non-connected ornon-contiguous areas may also be dynamically determined according tosimilar methods as a single contiguous/connected ROI.

Where a center point is used to derive an ROI, the center point on thechest may become blocked in some instances, such as when a hand moves infront of the determined center point of the chest. In that instance, theROI may erroneously track the hand, instead of the chest. In order tocounteract this, the system may monitor the center point to ensure thatit has good respiratory modulation, i.e. that the center point movessimilarly to a human breathing. If that center point (or any other pointused) ceases to move with a frequency akin to human respiratorymodulation, a new center point may be sought, where human respiratorymodulation is occurring. Once such a new point is identified, the regionaround that point can be filled in to form a new ROI. In someembodiments, that method may be used to find a point around which theROI should be filled-in in the first instance (rather than attempting tolocate a center point of the chest).

In some embodiments, multiple points that show a characteristic similarto respiratory modulations may be selected and used to fill out one ormore ROIs on a body. This can advantageously result in identifying anypart of the body, not just a chest area, that moves as a result ofbreathing. Additionally, this method can advantageously provide multipleROIs that may be monitored together to measure tidal volume orrespiration rate, or extrapolated to measure tidal volume as if therewere only a single ROI. For example, an arm blocking a camera's view ofa chest may extend all the way across the chest. The system can thenidentify at least two points typical of respiratory modulations, oneabove the arm on the chest and one below the arm on the chest. Two ROIscan be filled out from those points to extend to cover the chest that isnot visible to the camera.

That measured data can then be extrapolated to account for the amount ofchest blocked by the arm to get a more accurate tidal volumemeasurement. The extrapolation may also account for the portion of thechest that is being blocked. This may be helpful because different partsof the chest will move to different degrees than others during a breath.The two ROIs above and below may be utilized to determine which part ofthe chest is being blocked by the arm. For example, if the top ROI isvery small and the bottom ROI is comparatively larger, the system candetermine that the arm is blocking a higher portion of the chest closerto the neck. If opposite (large top ROI and small bottom ROI), thesystem can determine that the portion of the chest being blocked isfurther down toward the waist. Therefore, the system can account forwhich part of the chest is being blocked when calculating tidal volume.

In order to extract accurate volume changes from a breathing patientusing a depth sensing camera, it is important to correctly select thesampling region, which is then used to aggregate the volume changes. AnROI that encompasses as much of the patient's trunk as possible canadvantageously be more accurate than a smaller ROI in capturing completerespiratory motion of a patient. Accordingly, an ROI may be dynamicallyselected, so that an optimum sampling region based on depth data andskeleton coordinates is continually determined and refreshed asdescribed below.

FIG. 9 is a diagram showing a patient 905 with a superimposed skeleton910 according to various embodiments described herein. Depth data from adepth sensing camera and inferred skeletal information are presented inFIG. 9 . Positions from the skeleton data can be used to define abreathing ROI (the rectangle) in which it is safe to expect to findstrong respiratory modulation. This breathing ROI is made to extend fromboth shoulder joints (each indicated by a dot at the top corners of therectangle), and down to a mid-spine joint (indicated by a dot in themiddle of the bottom line of the rectangle). The shading within theimage indicates depth information: the darker gray that outlines a bodyis relatively closer to the camera, while the lighter gray on the wallsrepresents portions of the image that are farther from the camera. The3D information in an image may be encoded in a way that allows forgreater contrast than can be shown in the gray scale images of FIGS.9-21 . For example, the depth information may be shown using RGB datapoints. In another example, pixels or coordinates of an image may beassociated with a depth value that is used to calculate tidal volumeaccording to the systems, methods, and computer readable media disclosedherein.

FIG. 10 is a diagram showing a patient with a superimposed skeleton andROI according to various embodiments described herein. A two-dimensionalbody mask 1005 can also be inferred from the skeletal coordinates andencompasses the breathing ROI. The two-dimensional body mask 1005 isdefined in FIG. 10 to encompass the patient's trunk by using a dilatedpentagon with corners located at: 1) right shoulder, 2) right hip, 3)left hip, 4) left shoulder, and 5) neck joint (at or near cervicalvertebrae C7). In various embodiments, other shapes, dilations, or othershape modifications may be used to determine the two-dimensional bodymask. In some embodiments, a shape for determining the two-dimensionalbody mask may be selected based on the shape of the patient's body,demographic data of the patient, an orientation of the patient's body,or any other factor. The mask here is a reasonable approximation of theactual torso boundaries within the 2D depth image (the data in a 2Ddepth image encodes 3D information so that changes in depth in a 3Dspace can be detected and utilized to calculate tidal volume asdisclosed herein).

FIG. 11 is a diagram showing a patient with an ROI turned to face afirst direction (patient facing toward the right on the page) accordingto various embodiments described herein. FIG. 12 is a diagram showing apatient with an ROI turned to face a second opposite direction (towardthe left on the page) according to various embodiments described herein.As shown in FIGS. 11 and 12 , the dynamically-generated mask can followrotations of the torso relative to the camera.

FIG. 13 is a diagram showing a patient with an ROI that has been floodfilled according to various embodiments described herein. Atwo-dimensional depth mask can also be created from the depth imageusing a depth-based flood fill method. In other words, parts of theimage that are within a certain depth range from the camera are floodfilled to represent the ROI. A seed coordinate is place within thebreathing ROI. In this case, the center of the box was used. A depthtolerance range can be defined relative to the seed point's depth fromthe camera: a low tolerance defines the closest allowed pixel, and ahigh tolerance defines the furthest allowed pixel to be included in theROI. A flood fill method is applied starting from the seed to find thelargest contiguous region contained with that range. This method canidentify the patient's chest, when the chest surface is somewhat planarand lies within the specified depth range from the camera. This methodcan determine hard boundaries of objects as shown in FIG. 13 . However,in this particular instance, regions of the patient's body which are notof as great an interest for a respiratory signal (e.g., head, arms) mayalso be included if they also fall within the same specified depthrange. Such regions can be excluded from the ROI if they do not exhibitrespiratory modulations.

FIG. 14 is a diagram showing an implementation of a depth mask todetermine an ROI according to various embodiments described herein. Inparticular, FIG. 14 shows how a seed point of the patient existsrelative to the depth camera, and how the high and low thresholds forthe depth mask may be configured. The “low” threshold sets the distancetoward the camera from the seed point, and the “high” threshold sets thedistance away from the camera from the seed point. Pixels that fallwithin these ranges will be included in the ROI. In various embodiments,different thresholds for the high and low thresholds may be utilized.

FIG. 15 is a diagram showing a patient with an ROI turned to face afirst direction, where the ROI has been flood filled but discards thearms according to various embodiments described herein. FIG. 16 is adiagram showing a patient with an ROI turned to face a second direction,where the ROI has been flood filled but discards the arms according tovarious embodiments described herein. The flood field is able to handlerotation of the patient because as the patient turns, the patient's armsmove too close or far from the camera, and thus move out of thethresholds of the depth mask. Accordingly, the dynamically generatedflood field ROI is able to discard obstruction caused by the arms basedon the depth range defined. In particular, in both FIGS. 15 and 16 , thechest remains within the ROI while the arms are excluded.

FIG. 17 is a diagram showing a patient with an ROI that does not includethe patient's hand according to various embodiments described herein.FIG. 17 shows another example of the flood field ability to discardobstruction based on depth values (i.e., using a depth mask). Thepatient's hand is correctly discarded from the generated ROI because itis too close to the camera.

FIG. 18 is a diagram showing a patient with an ROI where the arms andhead have been excluded according to various embodiments describedherein. In particular, the ROI in FIG. 18 uses a combination of the bodymask described above with respect to FIGS. 9-12 and the depth maskdescribed above with respect to FIGS. 13-17 in order to generate animproved sampling region (ROI) from which to extract respirationvolumes. In other words, both the methods are applied to an imagecaptured by a camera to get a more accurate ROI, leading to more preciseand/or accurate tidal volume measurements. FIG. 18 shows an example ROIwhere the patient is facing the camera (generally orthogonal to thecamera's line of sight), that is generated/determined using both methodscombined.

FIG. 19 is a diagram showing a patient with an ROI where the arms andhead have been excluded and the patient is turned to face a firstdirection according to various embodiments described herein. FIG. 20 isa diagram showing a patient with an ROI where the arms and head havebeen excluded and the patient is turned to face a second directionaccording to various embodiments described herein. When the patient isrotated as in FIG. 19 or FIG. 20 , the mask created with the combinedmethod performs better than either of the methods in isolation. There isno overflow of the region that could occur with the flood fill, so thehead, arms, chair, etc. are correctly discarded. However, the flood fillmethod's robustness to boundary obstructions is preserved. FIG. 21 is adiagram showing a patient with an ROI that does not include thepatient's hands according to various embodiments described herein.Accordingly, as disclosed herein, various features—the hands, face,etc.—may be identified in the image and filtered out of the ROI on thatbasis. In some embodiments where obstructions are present, the visible,unobstructed ROI area may be measured and matched to an ideal area (ifthe whole ROI was visible), and the measured area (visible, unobstructedarea) divided by this value (the ideal ROI area) to give an equivalentproportional area for use in a total tidal volume estimation.

With respect to FIGS. 22-25 described below, a true tidal volume may bedetermined by adjusting a measured non-contact or video tidal volumeaccording to historically collected data which shows a relationshipbetween the non-contact monitoring tidal volume and the reference (thehistorically collected data). FIG. 22 is a graph showing tidal volumemeasured by a reference air flow measurement device (x-axis) as comparedto tidal volume measured by non-contact video monitoring (y-axis)according to various embodiments described herein. In FIG. 22 , over 100breath volumes determined by a camera system are plotted against volumesdetermined from a reference air flow meter device. The figure shows avery clear linear relationship between the two data sets, with anon-identity slope (a slope that is not equal to 1). Thus, a video tidalvolume measured from a non-contact video system can easily be translatedinto an expected true tidal volume by multiplying by a coefficient basedon the slope.

A line is fitted to the data. This line may be in the form of a linearregression line with the form of Equation 3 below:TVm=m×TVr+c  [3]where TV_(m) is the measured tidal volume using the non-contact camerasystem, TV_(r) is the reference tidal (true) volume, m is the gradientand c is a constant. In such a method, a regression may be used wherethe line is forced through the origin of the graph in FIG. 22 . Thisyields Equation 4 below (i.e., c=0):TV _(m) =m×TVr  [4]and the gradient m becomes a simple multiplier constant. Alternatively,a more complex, non-linear equation may be fitted to the data.Alternatively, a piecewise function may also be fitted, or any otherrelationship. In various embodiments, a series of relationshipsdepending on other factors may be utilized. For example, differentcurves or fits may be utilized for various respiratory rates, variouspatient postures, modes of breathing (chest or abdominal), patientdemographics (BMI, age, sex, height, weight, etc.), or any other factor.

The tidal volume measurement (TV_(m)) may also be used to determinewhether a patient is exhibiting hypoventilation. FIG. 23 is a graphshowing tidal volume measurements and a respiratory compromise thresholdaccording to various embodiments described herein. In FIG. 23 , a plotof TV_(m) against the measured minute volume (MV_(m)) is shown. Minutevolume is the amount of air breathed by a patient per minute. Thisinformation is valuable because patients may breathe at different ratesand depths (some may breathe longer and deeper, while others breatheshallower but more often). However, the minute volume indicates how muchtotal air is actually being taken in by a patient over time, which canbe valuable to indicate whether a patient is in a normal state (e.g.,normoventilation) or abnormal state (e.g., hypoventilation,hyperventilation). A distinct kink in the data at the respiratorycompromise threshold indicates a lower threshold of normoventilation,below which hypoventilation may be taking place. Above this point theminute volume is relatively constant with increasing tidal volume,increasing only slightly. This relatively constant region indicates thateven at larger tidal volumes, minute volume is relatively stable, likelybecause larger breaths (with larger tidal volume) are taken at lowerrespiratory rates (breaths per minute), leading to a similar totalminute volume. Such a plot may indicate to a clinician that the patientis exhibiting hypoventilation and that an intervention is necessary.

A threshold minute volume may also be determined as shown in FIG. 24 .FIG. 24 is a graph showing tidal volume measurements and a thresholdminute volume on the y-axis, indicating hypoventilation according tovarious embodiments described herein. In other words, a threshold minutevolume may be determined that indicates a patient may be in thehypoventilation region. In some embodiments, a moving average may beused since some of the data points in the normoventilation region fallbelow the threshold minute volume. Hypoventilation can be determined tobe present when a patient's tidal volume falls below the x-axisrespiratory compromise threshold (e.g., a threshold tidal volume), orthe minute volume falls below the y-axis threshold minute volume, or acombination of both, for a minimum duration of time. Whenhypoventilation is determined, the system may generate an alarm toindicate to healthcare professionals that the patient should bemonitored and/or treated.

FIG. 25 is a graph showing a measured minute volume that can be used tocalculate a degree of compromise according to various embodimentsdescribed herein. Once below the threshold(s), a degree of compromisemay be represented by a ratio of areas as shown on the plot in FIG. 25 .That is, the area indicated by the dotted lines can be divided by thearea indicated by the solid lines to give an indication of the severityof the respiratory compromise. The dotted lines show where the patient'smeasurements currently are, and the solid lines indicate the thresholdfor normal respiration. This ratio can be determined by dividing themeasured minute volume by the threshold volume level as shown in FIGS.24 and 25 and as follows in Equation 5:CD=MV/MV _(threshold)  [5]or alternatively using the measured tidal volume and the respiratorycompromise threshold (e.g., the threshold tidal volume) as shown belowin Equation 6:CD=TV/TV _(threshold)  [6]It can be seen that these ratios are the same when a data point falls onthe fitted line and the fit is linear and goes through the origin.However, they may differ due to a data spread or if other non-linearforms are used. These graphs may be generated on a patient by patientbasis to generate custom lines and thresholds, or curves may be appliedto tidal volumes measured through non-contact video monitoring that aremost likely to fit a patient as disclosed herein.

As mentioned above, the volume signal V(t) from the video image may needto be calibrated or adjusted to obtain a true tidal volume. For example,the image in FIG. 3 above was captured with the patient sitting withtheir back pressed against a seat and facing the camera. Accordingly,the plane of the chest of the patient is orthogonal to the camera.Disclosed herein are methods for calculating a tidal volume in instanceswhere the plane of a patient's chest is not orthogonal to a camera'sline of sight.

If the patient is sitting at an angle to the camera, a motion vectorassociated with respiration of the patient may not be in line with thecamera's line of sight. FIG. 26 is a diagram showing an ROI with a floodfill region according to various embodiments described herein. FIG. 26shows the skeleton superimposed onto the depth image of the patient.Also shown in FIG. 26 is the flood fill region of the ROI. In thisembodiment, the ROI is defined within a distance from the center of thechest. Such method works well if the chest is orthogonal to the line ofsight of the camera.

FIG. 27 is a diagram showing a patient at an original position accordingto various embodiments described herein. FIG. 28 is a diagram showing apatient at an angle to a line of sight of a camera according to variousembodiments described herein. In other words, FIG. 28 shows the floodfill region on the patient once he/she has rotated to sit at an angle tothe camera's line of sight. Comparing this region with the original inFIG. 27 , the flood fill region has moved onto the side of the patientcovering part of the left arm and moving away from the right-hand partof the chest.

An improved method is disclosed herein for correcting this movement ofthe flood fill region caused by a non-orthogonal angle of the plane ofthe chest to the line of sight of the camera. FIG. 29 is a diagramshowing a representation of a patient from above according to variousembodiments described herein. FIG. 30 is a diagram showing arepresentation of a patient at an angle to a line of sight of a camerafrom above according to various embodiments described herein. FIG. 29shows the patient with their chest plane orthogonal to the line of sightof the camera. Respiratory displacements of the chest are shown. Theserespiratory displacements are denoted as d_(i,j), where i and j are theindices along the vertical and horizontal plane of chest. Thesedisplacements are integrated across the ROI to provide a tidal volumefrom the depth camera system. FIG. 30 shows the patient sitting at anangle (θ) to the line of sight. In this case, the displacements alongthe line of sight of the camera d*_(i,j) will be less than the actualdisplacements orthogonal to the chest wall. We may correct thesedisplacements by dividing by the cosine of the angle θ as follows inEquation 7:di,j=d*i,j/cos(θ)  [7]The true tidal volume in the direction of the line of sight may now becalculated by numerically integrating these values according to Equation8 below:TVc=Σ _(i)Σ_(j) d _(i,j)Δ  [8]where Δ is the area of the i-j grid tiles. This type of measurement canalso be performed if the patient is reclining; that is, if the rotationof the plane of the chest is along a different axis or plane (e.g. alongan x axis rather than a y axis as in FIG. 30 ). Additionally, this typeof measurement can be performed if the rotation of the plane of thepatient's chest is along multiple axes. These, however are merelyexamples, and it will be understood that further enhancements to theseformulas can be made to account for a twisting of the patient along thetorso from shoulders to hips.

The embodiments described above with respect to FIGS. 29 and 30 assumethat the volume change of the ROI is solely in a direction orthogonal tothe plane of the chest wall. Additional correction factors may be usedto take account of the breathing which expands the torso in lateraldirections. These correction factors may be applied irrespective of aposition or orientation of the chest to the camera.

FIG. 31 is a diagram showing apparent movement of an ROI of a patientorthogonal to a line of sight of a camera according to variousembodiments described herein. In other words, the surface of thepatient's chest is oriented orthogonal to the line of sight of thecamera, and the movement shown is movement, as seen by the camera, ofthe chest of the orthogonally oriented patient as that patient breathes.FIG. 32 is a diagram showing apparent movement of an ROI of a patientthat is not orthogonal to a line of sight of a camera according tovarious embodiments described herein. In other words, the surface of thepatient's chest is oriented non-orthogonally with respect to thecamera's line of sight, and the movement shown is movement, as seen bythe camera, of the chest of the non-orthogonally oriented patient asthat patient breathes. In an embodiment, the lateral motion associatedwith the chest movement non-orthogonal to the camera line of sight (FIG.32 ) can be accounted for. The ROI seen by the camera system in FIG. 32is compressed in the horizontal direction due to when the patient isnon-orthogonal to the line of sight of the camera. As the patientbreathes, the apparent position of the ROI will move due to thehorizontal component of the chest displacements (this is zero for aperfect orthogonal case (FIG. 31 ) which has no such movement). Knowingthe angle θ, the change in the location of a characteristic points onthe ROI may be calculated and thus the ROI through the respiratory cyclemay be more accurately tracked.

FIG. 33 is a diagram showing an angle at which a patient's ROI is notorthogonal to a line of sight of a camera according to variousembodiments described herein. A transformed flood field box can bedefined by knowing the angle θ as shown in FIG. 33 . Surface outsidethis box may not be included in the flood field. Furthermore, as shownin FIG. 33 , the thresholds from a center point on the chest may stillbe utilized as adjusted according to the angle θ.

In some embodiments, the flood field depth range may be increased inmagnitude by using the angle of incidence and/or the location of theperipheral (shoulder) point on the skeleton as illustrated in FIGS.34-37 . FIG. 34 is a diagram showing a representation of different depththresholds associated with a patient orthogonal to a line of sight of acamera according to various embodiments described herein. FIG. 35 is adiagram showing a representation of unadjusted depth thresholds withrespect to a patient that is not orthogonal to a line of sight of acamera according to various embodiments described herein. FIG. 36 is adiagram showing a representation of adjusted depth thresholds withrespect to a patient that is not orthogonal to a line of sight of acamera according to various embodiments described herein. FIG. 37 is adiagram showing an alternate method for adjusting depth thresholds withrespect to a patient based on locations of shoulders of the patientaccording to various embodiments described herein (e.g., that can beemployed to ensure that the two shoulder joints of the patient alwaysstay inside the flood fill range).

In particular, the thresholds H and L of FIGS. 34 and 35 are adjusted toH2 and L2 of FIG. 36 based on the angle θ. In another embodiment shownin FIG. 37 , H2 and L2 are adjusted from H and L based on known pointsof the body, such as the shoulder joints represented by the red crossesof FIG. 37 . In a first example, H2 and L2 are adjusted according to afixed tolerance amount, such as by adjusting H2 and L2 according toEquations 9 and 10, respectively, below:H2=MAX(H,DISTANCE(SEED,FAR SHOULDER)+TOLERANCEAMOUNT)  [9]L2=MAX(L,DISTANCE(SEED,NEAR SHOULDER)+TOLERANCEAMOUNT)  [10]In a second example, H2 and L2 are adjusted according to a relativeamount (e.g., 10%), such as by adjusting H2 and L2 according toEquations 11 and 12, respectively, below:H2=MAX(H,DISTANCE(SEED,FAR SHOULDER)*1.1)  [11]L2=MAX(L,DISTANCE(SEED,NEAR SHOULDER)*1.1)  [12]This helps ensure that the motion of the chest is properly captured andthat the ROI is properly determined such that tidal volume can beaccurately calculated.

The discussion below with respect to FIGS. 38-43 further discuss how toaddress obstructions in the line of sight between a camera and a desiredROI on a patient. In some cases when the patient is completely obscuredby an obstruction, a tidal volume output may be reported as invalid.However, in some cases with partial obstructions, for example from thehands of the patient moving in front of the camera, an ROI may beadjusted so that an accurate tidal volume can be determined. Variousembodiments disclosed herein advantageously provide improvements forovercoming partial obstructions using a three-dimensional (3D)calibration procedure prior to real-time monitoring of tidal volumeusing a depth sensor camera system. In some embodiments, a hand may beresting flush with the chest. In such an instance, the hand may not beexcluded from the ROI, as it may move along with the chest as thepatient breathes. In some embodiments, the area where the hand is placedmay be incorporated in a measurement of the tidal volume, but may beassigned a lower confidence value or excluded if the movement in thearea of the hand differs significantly from movement of the chestshowing around the hand. That is, the system may determine when the areaof the hand can be used to accurately calculate tidal volume and when itshould be excluded.

FIG. 38 is a diagram showing an ROI of a patient according to variousembodiments described herein. FIG. 39 is a diagram showing an ROI of apatient with a partial obstruction of the patient's hands according tovarious embodiments described herein. FIGS. 38 and 39 show the depthdata obtained using a depth camera sensor as disclosed herein, showingthe ROI without any obstruction in FIG. 38 and with partial obstructionof the ROI in FIG. 39 .

In an embodiment, a 3D body scan calibration process is performed at thestart of measurement for the patient. FIG. 40 is a diagram showing apatient with a three-dimensional (3D) mesh superimposed over the patientaccording to various embodiments described herein. The 3D mesh isobtained from a calibration process that allows the mapping of a 3Dchest surface profile of the patient. This calibrated 3D surface profileis used to estimate a portion of an ROI that has been obscured. Theobscured region is identified, and the 3D profile is used to estimatethe contribution to the tidal volume of the obscured region according tovarious embodiments discussed below.

In a first embodiment, the ratio of the original unobscured ROI (Au) tothe visible area may be used to estimate the true tidal volume (TVe)from the measured tidal volume from the visible area (TVv)) as followsin Equation 13:TVe=TVv(Au/(Au−Ao))  [13]where Ao is the obscured area. This is shown schematically in FIG. 41 ,which is a diagram showing an ROI of a patient with an obscured areaaccording to various embodiments described herein.

In other embodiments, the excursions around the obscured area may beused to estimate the excursions within the obscured area which are thenmultiplied by the obscured areas to provide the contribution to measuredtidal volume from the unobscured area. This is shown schematically inFIG. 42 , which is a diagram showing an ROI with an excluded obscuredarea according to various embodiments described herein. This may be doneby measuring the average excursion (Δ_(ave)) around the edge of theobscured region and using this to calculate the tidal volumecontribution (TVc) as follows in Equation 14:TV _(c) =A _(o)×Δ_(ave)  [14]where Ao is the area of the obscured region. Alternatively, the relativeexcursions during the pre-obscured time within the obscured region aredetermined and used to estimate the excursions during the obscured time.This may be done by assigning excursion pro-rata based on proportionalexcursions across the mesh during the pre-obscured period.

In another embodiment, the data from the last previously unobstructedbreath can be saved as a map of relative contribution to the measuredtidal volume. An obstructed region's contribution can be calculatedusing this historical unobstructed map of ratios. Moreover, a confidencemetric of the estimate can be deduced using this map, where, forexample, C=1−Sum(Obstructed contributions). In this way, obstruction oflow contribution areas would affect confidence less than obstruction ofareas known to contribute more to the measured volume. In the absence ofprevious unobstructed breath, a generic map of contribution can be usedwhich would be built based on accumulated patient data.

In another embodiment, feature points measurements (e.g., skeletalpoints such as shown in FIGS. 38 and 39 ) are recorded during thecalibration process. These feature points represent fixed physicaldimensions that will be used to calculate the position of the 3D bodymesh due to the changes in orientation of the patient. Some examples offixed point measurements are sternum to shoulder ends, height of chest,and width across stomach/belly/waist. If the total obscured region iswithin an acceptable tolerance, then the obscured region isreconstructed using the initial 3D mesh. The estimated 3D surface can beperformed by comparing the unobscured regions with the 3D calibrationscan, and re-mapping the obscured regions after obtaining the bestmorphological transform of the current position of the body. (This canbe a translation, rotation, affine transformation due to different bodyposition and respiration.)

In another embodiment, a reconstructed region is displayed in adifferent color scheme to the normal depth data. This provides a visualfeedback to the operator which indicates the region that is based onestimated calculation. This is shown in FIG. 43 , which is a diagramshowing an ROI with an excluded obscured area according to variousembodiments described herein. In particular, the larger, light grayregion 4305 is a normal ROI covering the full chest region and thesmaller oval with diagonal lines region 4310 indicates an obstructionpresent in that instance of measurement. A confidence level may also becalculated based on, for example, the ratio of visible area to totalarea. The confidence level may be displayed on the screen and/or may beused within the tidal volume algorithm. For the latter, it may, forexample, be used to determine when the confidence is below a thresholdand therefore the tidal volume should no longer be displayed.

Also disclosed herein are various systems, methods, and computerreadable media for improving tidal volume measurements using non-contactvideo monitoring. For example, a volume signal may be corrupted withnoise due to movement of the patient. In another example, certainmovement of a patient related to respiration may not always be visibleto a camera. Disclosed herein and discussed below with respect to FIGS.44-46 are embodiments for mitigating noise and improving accuracy androbustness of tidal volume measurements.

FIG. 44 is a diagram showing a two-camera system for determining ROIs ofa patient and/or measuring tidal volume of the patient according tovarious embodiments described herein. In a multiple camera system,cameras may be oriented at the back and front of the patient as shown inFIG. 44 . Such cameras can be used to produce two volume signals usingvarious embodiments disclosed herein: V1(t) and V2(t). In a method V1(t)and V2(t) may be used to determine an actual tidal volume by subtractingone from the other. For example, the volume change signal may bedetermined as follows in Equation 15:VC(t)=V1(t)−V2(t)  [15]The initial values of VC(t) may be set to zero when the analysis isfirst activated. Alternatively, the minimum value of VC(t) may be set tozero. The method is outlined schematically in FIG. 44 . In variousembodiments, more than two cameras may be used to further improve thetidal volume measurement. In the example shown in FIG. 44 , the volumesignals V1(t) and V2(t) are associated with a first camera on the leftand a second camera on the right, respectively. The signals V1(t) andV2(t) each trend up if they are configured such that the positivedirection for each camera is the same. For example, if the positivedirection for each camera is set as left to right in FIG. 44 then thesignals V1(t) and V2(t) indicate that the patient is moving toward thecamera on the right while breathing. If the positive direction for eachcamera is set as right to left in FIG. 44 , then the signals V1(t) andV2(t) would indicate that the patient is moving toward the camera on theleft while breathing. If, however, the cameras were set so that thepositive direction was relative to each camera, the signals V1(t) andV2(t) would trend in opposite (rather than the same as in FIG. 44 )directions when the patient moves toward one of the cameras and awayfrom the other.

A multiple camera system may also be beneficial to track and measureshoulder movement. For example, in some patients, tidal volume may bemeasured at least in part by monitoring the movement/displacement of theshoulders. A depth sensing camera oriented generally orthogonal to apatient's chest may be able to detect some shoulder movement for thepurpose of measuring tidal volume. However, one or more additionalcameras (e.g., above a patient, to the right or left of a patient,behind a patient) may be able to capture additional movement in theshoulders that can be used to measure tidal volume.

Multiple camera systems can also be advantageously used to removenon-clinically relevant data. For example, patients may move throughouta room or in bed in a way that would impact the measurements made by asingle camera and make it difficult to measure tidal volume. Byutilizing multiple cameras, the movement of the patient can be tracked.For example, if a patient moves toward one camera and away from another,the depth vector measurements from the two cameras will capture thatmovement data in opposite directions and cancel one another out, leavingthe movement associated with breathing to be measured as tidal volume.In such an embodiment, the system may determine an ROI on the chest ofthe patient using data from the first camera and a second ROI on theback of the patient using data from the second camera. Systems usingmore than two cameras in a similar way may also be used, and may addfurther robustness to the system.

In order to use two or more cameras to assess the patient's movement,position, and volume changes, in an embodiment, the cameras are able todetermine where they are positioned and oriented with respect to eachother. For example, in order to combine the depth measurements from eachcamera, the system needs to know if the two cameras are viewing inopposite directions, orthogonal directions, or any other angle ororientation. Because the tidal volume calculations can be made based onvectors in x, y, and z axes, the system can identify a calibrationpoint(s) in the room to adequately define the axes, which may beparticularly useful in embodiment where multiple cameras do not haveline of sights that are orthogonal to one another. The cameras candetermine their relative orientation by viewing a common object orcalibration point in the room. That is, in one embodiment, an object orpoint in the room is visible within the field of view of both (or all)cameras. A calibration point may be a point on the patient such as a topof the head, or may be something in the room. The point identified inthe room may be a specially configured device such as a sticker or signwith a bar code or other feature on it that can be recognizable fromdata captured by a camera. By identifying the same point or points inthe room and using depth sensing data to determine where the camera iscompared to the known object, point, or points, the system canaccurately determine how measurements from each depth sensing camera canbe mapped into vectors on the x, y, and z axes. In other words, thepoint(s) in the room can be used to identify where the cameras areactually located, and where the cameras are located with respect to oneanother.

In some embodiments, the cameras may send communications that can becaptured by one another in order to calibrate them. For example, acamera may flash a light or send another signal to indicate itsposition. In another example, the depth sensing camera may capture dataindicative of a camera so that the system can determine the location ofa camera within another camera's field of view. This information canalso be used to synchronize the data captured, i.e., make sure movementcaptured by the cameras are mapped as vectors onto the same axes so thattidal volume can be accurately determined. A three-dimensional object inthe room may also be identified and used to calibrate/locate thecameras. In other words, information about the object in the room can beused to figure out where the cameras are in relation to the object andtherefore in relation to one another. If a camera moves or is adjustedin a way that affects its field of view, zoom, etc., thatmovement/adjustment can be tracked and accounted for whencalibrating/locating the cameras and subsequently in tidal volumecalculations.

In some embodiments, multiple cameras may be able to see an entire roomor more. The system may include logic to use or prioritize data fromcertain cameras that have a better view of a patient or ROI. In thisway, more accurate measurements can be made. If multiple cameras areused to determine ROI and/or tidal volume, some cameras may bedetermined to have a better view of the patient or otherwise can makemore accurate measurements. In such cases, the system may weight thedata from those cameras more heavily (assign it a higher weight) orassign it higher confidence levels, so that the data that is more likelyto be accurate is prioritized when calculating a tidal volume or othermetric.

Similarly, various embodiments may also utilize full 3D reconstructionusing multiple depth cameras. The real time reconstruction of a 3Dvolume based on multiple depth cameras can be used to track the overallvolume of a patient in real time. In other words, rather thandetermining ROIs on the patient's body, the system may track the entirebody of a patient. The tidal volume is a component of the patient'soverall volume and may be extracted as a proportion of the total volumechange. The motion (skeleton detection/tracking) data provided by thevarious embodiments disclosed herein can be used to mitigate againstchanges caused by patient motion.

In various embodiments, a multiple ROI method using a single camera mayalso be used. A larger ROI may be used as well as a smaller ROI (e.g.,the chest only ROI). The mean movement of the larger ROI may be used tofilter out the global body motions from the chest ROI hence leaving therespiratory signal intact. This may be done by using an adaptive filterto remove from the chest ROI signal the non-respiratory motionsidentified in the larger ROI signal. The larger ROI may or may notinclude the chest ROI. An example of this embodiment is shownschematically in FIG. 45 showing a patient with two differently sizedROIs for measuring tidal volume according to various embodimentsdescribed herein.

Other filtering/processing may be performed to exclude information thatis non-clinically relevant. For example, when patients are talking oreating they may have unusual tidal volumes and respiration patterns thatare harder to track and may not be clinically relevant. Accordingly, thesystems, methods, and computer readable media disclosed herein may beconfigured to identify periods where a patient is talking or eating ordoing another activity which is desirable to exclude. For example, datafrom a depth sensing camera may indicate that the patient is talking:movement of mouth/lips, irregular respiration rate, etc. Other sensorsmay be used in conjunction with the camera to determine that a patientis talking, such as an audio sensor. If an audio sensor picks up audiotypical of the human voice and the respiration rate is abnormal, forexample, the system may identify that the patient is talking and not usethe data collected to attempt to monitor or calculate tidal volume.Other irregular situations may also be identified, such as while apatient is eating. Depth sensing camera data may be used to determinethat the patient is eating, for example through movement of the jawsimilar to chewing, neck movement indicating swallowing, hands movingperiodically to the mouth to feed, appearance of a straw-like shape infront of the patient's face, etc. By identifying instances whereirregular breathing is likely, the system can filter out data collectedduring those periods so as not to affect tidal volume measurements,averages, or other calculations. Additionally, the determinations ofscenarios like eating and talking where breathing is expected to beirregular may also be beneficial for alarm conditions. For example, in ascenario when a patient is talking, any alarm related to a tidal volumemeasurement may be suppressed by the system.

FIG. 46 is a flowchart for a method 4600 for determining tidal volumeusing two differently sized ROIs according to various embodimentsdescribed herein. The method 4600 includes a video signal 4605, fromwhich a larger ROI is determined at 4610 and a smaller chest ROI isdetermined at 4615. The method 4600 further includes filtering the chestROI at 4620. At 4625, the tidal volume of the patient is output.

Various embodiments may include filtering out non-physiological signalsas disclosed herein. For example, an expected spectral bandwidth ofbreathing may be known and used to filter out non-respiratory signalsfrom a volume signal. For example, a raw volume signal may be band-passfiltered between 0.10 and 0.66 Hz (corresponding to 10 second and 1.5second breaths or 6 and 40 breaths per minute). Where movement fallsoutside of the frequency range, it may be excluded because it isunlikely to be movement associated with respiratory movement.

In some embodiments, the systems, methods, and computer readable mediadisclosed herein may be used to measure volumetric CO₂. For example,when used in conjunction with a nasal cannula or other capnographydevice, volumetric CO₂ can be determined. In particular, a capnographydevice measures the percentage of carbon dioxide in the air beingbreathed out by a patient. With a tidal volume measurement as disclosedherein, the percentage of carbon dioxide in the air can be multiplied bythe tidal volume to determine the volumetric CO₂ of the patient (i.e.,how much total volume of carbon dioxide the patient is breathing out).

Various other data processing and filtering processes may be used ondata gathered using depth sensing cameras or other devices formonitoring a patient. For example, trends may be monitored in the data,moving averages, weighted averages, and filtering to removenon-conforming data may all be utilized. Confidence levels may also beutilized to determine whether to include data. For example, anon-conforming behavior like talking may be identified to apredetermined threshold confidence level. If the non-conforming behavioris identified to that certain confidence level, then the data collectedduring that time can be excluded from trends, averages, and other dataprocessing and/or gathering operations performed by the system. Thesystem may also calculate confidence levels with respect to the tidalvolume being measured. For example, if a robust ROI is determined, thesystem may have a higher confidence level with respect to the tidalvolume calculated. If the patient is too obstructed, too far away, orother factors that are known to cause issues with tidal volumemeasurement is present, the system may associate a low confidence levelwith the tidal volume measurement. If a confidence level falls below aparticular threshold level, the data collected during that time can beexcluded from certain calculations with respect to the patient and theirtidal volume. In some embodiments, confidence level thresholds may alsobe used to determine whether to propagate an alarm or not. For example,if a patient has left the room, the system will measure zero tidalvolume. However, the system may recognize that it has not identified anROI, giving a zero-confidence level in that measurement. Accordingly,alarm conditions with respect to the zero-tidal volume measurement willbe suppressed. In more nuanced examples, the system may recognize whenirregular situations are occurring, and use confidence levels todetermine whether data collected is valid or invalid (i.e., should it beused in various calculations and/or recordkeeping of the system). Bydetermining whether certain data is valid or invalid, the system candetermine whether to use that data collected to calculate tidal volumeof a patient.

Disclosed herein are also various types of alerts that may be used inaccordance with tidal volume monitoring systems, methods, and computerreadable media. For example, an alert may be triggered when ahypoventilation as described herein is detected. An alert may also betriggered if a tidal volume falls below a predetermined threshold. Analert may be triggered if a minute volume falls below a predeterminedthreshold. An alert may be triggered if no breathing activity isdetected, or if no breathing activity is detected for at least a certainduration of time.

A system may also distinguish certain types of movement. For example, apatient's breathing patterns may change while sleeping. Accordingly, thesystem may determine if a patient is sleeping, how long they sleep,whether and how much they wake up in the night, etc. The determinationof certain types of movement may also be patient specific. That is,certain patients may move in different ways for different types ofmovement. For example, a sleeping patient A may move differently than asleeping patient B. The system may be able to identify differences insleep patterns between patients. The system may also be able to identifysleep and awake states of a patient, even if those states vary inmovement signatures by patient. For example, the system may identifythat a patient is awake based on breathing patterns, tidal volume,respiration rate, minute volume, and/or other factors. By monitoringthose factors, the system may be able to detect a change in thosefactors indicating that a patient is likely asleep. The system can thenstudy the sleeping times for trends to determine a signature of thatparticular patient while they are sleeping. The system can then watchfor data or signals similar to that signature in the future to determinethat the patient is asleep.

The systems and methods described herein may be provided in the form oftangible and non-transitory machine-readable medium or media (such as ahard disk drive, hardware memory, etc.) having instructions recordedthereon for execution by a processor or computer. The set ofinstructions may include various commands that instruct the computer orprocessor to perform specific operations, such as the methods andprocesses of the various embodiments described herein. The set ofinstructions may be in the form of a software program or application.The computer storage media may include volatile and non-volatile media,and removable and non-removable media, for storage of information suchas computer-readable instructions, data structures, program modules orother data. The computer storage media may include, but are not limitedto, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic diskstorage, or any other hardware medium which may be used to store desiredinformation and that may be accessed by components of the system.Components of the system may communicate with each other via wired orwireless communication. The components may be separate from each other,or various combinations of components may be integrated together into amedical monitor or processor, or contained within a workstation withstandard computer hardware (for example, processors, circuitry, logiccircuits, memory, and the like). The system may include processingdevices such as microprocessors, microcontrollers, integrated circuits,control units, storage media, and other hardware.

Although the present invention has been described and illustrated inrespect to exemplary embodiments, it is to be understood that it is notto be so limited, since changes and modifications may be made thereinwhich are within the full intended scope of this invention ashereinafter claimed.

What is claimed is:
 1. A method of determining tidal volume of apatient, comprising: receiving, using a processor, at least one imagecomprising depth information for at least a portion of the patient;determining, using the processor, a reference point on the patient;determining, using the processor, a region of interest based at least inpart on the reference point, wherein the region of interest correspondsto a trunk area of the patient; monitoring changes in the depthinformation in the region of interest over time; receiving, using theprocessor, demographic information about the patient; and determining,using the processor, a tidal volume of the patient based on themonitored changes in depth information and the received demographicinformation.
 2. The method of claim 1, wherein the region of interest isfurther defined based on at least one body coordinate determined fromthe reference point.
 3. The method of claim 2, wherein each of the atleast one body coordinate corresponds to a location on a body of thepatient, and wherein the location on the body of the at least one bodycoordinate is at least one of a shoulder, a hip, a neck, a chest, and awaist.
 4. The method of claim 1, wherein the region of interest isfurther determined based on a distance of various portions of thepatient from a camera that captures the at least one image.
 5. Themethod of claim 1, wherein the region of interest is further determinedby discarding various portions of a flood fill in response todetermining that the patient is rotated such that the patient is notorthogonal to a line of sight of a camera that captures the at least oneimage.
 6. The method of claim 1, wherein the region of interest isfurther determined by: determining that the trunk area of the patient ispartially obscured; and excluding a partially obscured region from theregion of interest.
 7. The method of claim 1, wherein the at least aportion of the patient is at least a first portion of the patient,wherein the at least one image is captured using a first camera, andwherein at least a second image comprising at least a second portion ofthe patient is captured using a second camera.
 8. The method of claim 7,further comprising determining, using the processor, a second region ofinterest of the patient based on at least the second image.
 9. Themethod of claim 1, further comprising, determining, using the processor,a second region of interest of the patient from the at least one image.10. The method of claim 9, wherein the region of interest is a differentsize than the second region of interest.
 11. A video-based method ofmonitoring a patient comprising: receiving, using a processor, a videofeed comprising a plurality of images captured at different times,wherein at least a portion of a patient is captured within the videofeed; determining, using the processor, a region of interest of thepatient on the video feed, wherein the region of interest corresponds toa trunk area of the patient; measuring, using the processor, changes tothe region of interest over time; determining, using the processor, atidal volume of the patient based on the changes to the region ofinterest; comparing, using the processor, the determined tidal volume toan output of an air flow measurement device to determine apatient-specific calibration factor, wherein the patient-specificcalibration factor is applied to a relationship between the determinedtidal volume and an adjusted tidal volume; and determining, using theprocessor, the adjusted tidal volume based on the determined tidalvolume and the patient-specific calibration factor.
 12. The method ofclaim 11, further comprising: receiving, using the processor,demographic information about the patient; and adjusting the determinedtidal volume based on the demographic information.
 13. The method ofclaim 12, wherein the demographic information comprises at least one ofa sex, height, weight, body mass index (BMI), and age of the patient.14. The method of claim 11, wherein a size of the region of interest isat least partially dependent on a distance of the patient from a camerathat captures the video feed.
 15. The method of claim 11, furthercomprising determining, using the processor, a change in the tidalvolume of the patient over time.
 16. The method of claim 15, furthercomprising determining, using the processor, a potential hypoventilationcondition based on the change in the tidal volume of the patient. 17.The method of claim 11, wherein the region of interest is configuredand/or the tidal volume of the patient is determined based on anorientation of the patient with respect to a camera that captures thevideo feed.
 18. The method of claim 11, wherein the video feed iscaptured using a first camera, and a second video feed is captured usinga second camera, wherein at least a second portion of the patient iscaptured within the second video feed.
 19. The method of claim 18,further comprising determining, using the processor, a second region ofinterest of the patient based on the second video feed, wherein thetidal volume is further determined based on changes to the second regionof interest over time.